The present invention relates to a pattern recognition and learning apparatus for obtaining a reference pattern used for recognition processing from input patterns of a specific category.
A pattern recognition apparatus for recognizing input patterns, such as voices or characters, has been developed. In the pattern recognition apparatus, an input pattern is compared with recorded reference or standard patterns, and the category of a reference pattern which has the highest similarity with the input pattern is obtained as a recognition result. In order to improve recognition capabilities, learning of reference patterns is necessary. New recording or registration of reference patterns in a dictionary memory is normally called learning of the reference patterns. A plurality of patterns of a known category, which are input for learning, are recorded or stored in a memory as reference patterns of the category in the same form as input patterns to be recognized.
In such a reference pattern recording method, since recognition processing is performed by a comparison (matching) between recorded or registered patterns and an input pattern, patterns uttered by the same talker at substantially the same time can be referred to as needed. However, in the case of the reference patterns registered in this method, a statistical distribution in patterns to be recognized is not taken into consideration. Therefore, the recognition method of this type is normally adopted for recognition of input patterns whose variation is small.
Recognition methods in consideration of the statistical distribution in patterns, such as Mahalanobis's recognition method using generalized distance system or a multiple similarity method, are known. In these methods, since reference patterns are made in consideration of the statistical distribution of patterns, a large number of sample patterns must be collected. In this case, statistical analysis of sample patterns is significantly difficult to achieve.
In the pattern recognition method of this type, since it is difficult to learn reference patterns, learning of patterns is performed in advance using a computer, and reference patterns thus prepared are recorded in a recognition apparatus.
In the multiple similarity method, the statistical distribution of patterns to be recognized is taken into consideration for preparing reference patterns. Therefore, recognition performance of this method is considerably superior to the recognition method wherein learning patterns are simply registered as the reference patterns. However, in order to prepare reference patterns in consideration of the statistical distribution, a method such as Karhunen-Loeve expansion (to be referred to as KL expansion hereinafter) is necessary, and this is not easy.
Known methods of KL expansion are the Jacobi method and Householder & Sturm method. The former method is relatively simple in computation procedure but requires a large volume of computation. The latter method requires a complex computation procedure and is difficult to apply to an apparatus.
Therefore, in a conventional apparatus, a high-performance computer separate from the pattern recognition apparatus is used to obtain reference patterns.